BaseFileLogger

class BaseFileLogger[source]

Bases: etna.loggers.base.BaseLogger

Base logger for logging files.

Create logger instance.

Inherited-members

Methods

finish_experiment(*args, **kwargs)

Finish experiment.

log(msg, **kwargs)

Log any event.

log_backtest_metrics(ts, metrics_df, ...)

Write metrics to logger.

log_backtest_run(metrics, forecast, test)

Backtest metrics from one fold to logger.

set_params(**params)

Return new object instance with modified parameters.

start_experiment([job_type, group])

Start experiment within current experiment, it is used for separate different folds during backtest.

to_dict()

Collect all information about etna object in dict.

log(msg: Union[str, Dict[str, Any]], **kwargs)[source]

Log any event.

This class does nothing with it, use other loggers to do it.

Parameters
  • msg (Union[str, Dict[str, Any]]) – Message or dict to log

  • kwargs – Additional parameters for particular implementation

log_backtest_metrics(ts: TSDataset, metrics_df: pandas.core.frame.DataFrame, forecast_df: pandas.core.frame.DataFrame, fold_info_df: pandas.core.frame.DataFrame)[source]

Write metrics to logger.

Parameters
  • ts (TSDataset) – TSDataset to with backtest data

  • metrics_df (pandas.core.frame.DataFrame) – Dataframe produced with etna.pipeline.Pipeline._get_backtest_metrics()

  • forecast_df (pandas.core.frame.DataFrame) – Forecast from backtest

  • fold_info_df (pandas.core.frame.DataFrame) – Fold information from backtest

Notes

If some exception during saving is raised, then it becomes a warning.

log_backtest_run(metrics: pandas.core.frame.DataFrame, forecast: pandas.core.frame.DataFrame, test: pandas.core.frame.DataFrame)[source]

Backtest metrics from one fold to logger.

Parameters
  • metrics (pandas.core.frame.DataFrame) – Dataframe with metrics from backtest fold

  • forecast (pandas.core.frame.DataFrame) – Dataframe with forecast

  • test (pandas.core.frame.DataFrame) – Dataframe with ground truth

Notes

If some exception during saving is raised, then it becomes a warning.

abstract start_experiment(job_type: Optional[str] = None, group: Optional[str] = None, *args, **kwargs)[source]

Start experiment within current experiment, it is used for separate different folds during backtest.

Parameters
  • job_type (Optional[str]) – Specify the type of run, which is useful when you’re grouping runs together into larger experiments using group.

  • group (Optional[str]) – Specify a group to organize individual runs into a larger experiment.